title: Disease diagnosis validation in TROPIX using CBR.
abstract: TROPIX is a practical application project initially designed to help improve health care delivery in the rural/semi urban clinics and public hospitals in Nigeria due largely to limited laboratory facilities, medical doctors, and expertise. This paper is devoted to the use of case-based reasoning (CBR) paradigm in concert with statistical association-based reasoning (ABR) for disease diagnosis, validation and therapy selection components of the research. Essentially, tentative disease diagnosis arrived at by some classification method using similarity and dissimilarity aggregate functions, the matched vector functions (MVF), aided by the application of evidence ratio factors (ERF) for tied match cases is passed to the CBR model for validation by reusing past similar cases. The design and organization of the case-library using singular value decomposition (SVD) technique on the disease-attribute decision matrix to generate primary/secondary storage key clusters, as well as the use of domain-specific case-object properties that help to build a good case-base are described in some detail. The paper presents a disease case validation algorithm for appropriate data filtering and therapy selection enhancement from the new case-base.
authors.affiliation: Center for Advanced Computer Studies, University of Southwestern Louisiana, Lafayette 70504, USA. sunny.okorie@inference.com
authors.forename: A S
authors.initials: AS
authors.lastname: Ochi-Okorie
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted; D005544:Forecasting; D006801:Humans